SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning
SEER enhances structured reasoning via reinforcement learning, achieving a 6.9% improvement on EntailmentBank.
Key Findings
Methodology
SEER employs reinforcement learning for structured reasoning, utilizing a structure-based return function to capture complex interdependencies between reasoning steps. A fine-grained reward function is introduced to delineate reasoning steps, particularly redundant ones. The method integrates generative models and supervised learning to optimize policy and critic.
Key Results
- On EntailmentBank, SEER outperforms existing RL methods by 6.9%, with an average improvement of 4.4% on the STREET benchmark, demonstrating exceptional efficiency and cross-dataset generalization.
- Compared to the state-of-the-art RL method, SEER improves by 5.8%/9.0%/6.0% in Task 1/2/3 on EntailmentBank.
- On the STREET benchmark, SEER achieves a 4.8% to 5.2% improvement over GPT-4 across multiple datasets.
Significance
SEER addresses the issue of neglecting logical dependencies between reasoning steps in existing methods, significantly enhancing the interpretability and trustworthiness of QA systems. This advancement has substantial implications for academia and industry, particularly in complex reasoning tasks.
Technical Contribution
SEER introduces the first structure-based return function capable of effectively capturing complex dependencies between reasoning steps, overcoming the limitations of traditional RL methods' chain structures. By incorporating generative models, SEER can reason without enumerating all possible actions.
Novelty
SEER is the first general framework to achieve tree and graph-structured reasoning via a structure-based return function, breaking the chain structure limitations of traditional RL methods and significantly enhancing reasoning performance.
Limitations
- SEER may require longer training times and significant computational resources for highly complex reasoning tasks.
- Performance on certain domain-specific datasets may not match that of specialized models.
Future Work
Future research could explore SEER's application in more complex reasoning tasks and optimize its computational efficiency. Additionally, integrating SEER with other AI technologies could enhance its performance in practical applications.
AI Executive Summary
In the field of artificial intelligence, interpretability and traceability are crucial features of question-answering systems. However, existing methods often neglect logical dependencies between steps in structured reasoning, resulting in inadequate interpretability. SEER significantly enhances the transparency and trustworthiness of the reasoning process by introducing a structure-based return function and a fine-grained reward function.
The core technology of SEER includes the integration of generative models and reinforcement learning, enabling reasoning without enumerating all possible actions. This innovation allows SEER to excel on both the EntailmentBank and STREET benchmarks, with improvements of 6.9% and 4.4%, respectively.
SEER's success lies not only in its technical innovations but also in its broad impact on academia and industry. Future research could further optimize SEER's computational efficiency and explore its application in more complex reasoning tasks.
Deep Analysis
Background
In recent years, explainable AI has made significant strides in the field of natural language processing. Representative works include Dalvi et al.'s EntailmentWriter and Ribeiro et al.'s STREET benchmark. However, these methods often overlook logical dependencies between steps in complex reasoning tasks, leading to inadequate interpretability.
Core Problem
In structured reasoning, models need to handle complex relationships between multiple reasoning steps. Existing methods often use chain structures, neglecting the logical dependencies of tree or graph structures, resulting in inaccurate and unreliable reasoning outcomes.
Innovation
SEER introduces a structure-based return function, achieving tree and graph-structured reasoning for the first time. This innovation overcomes the limitations of traditional RL methods' chain structures, significantly enhancing reasoning performance. Additionally, SEER integrates generative models and supervised learning to optimize policy and critic.
Methodology
- �� Structure-based Return Function: Captures complex dependencies between reasoning steps.
- �� Generative Model: Enables reasoning without enumerating all possible actions.
- �� Fine-grained Reward Function: Precisely delineates reasoning steps, particularly redundant ones.
Experiments
Experiments were conducted on the EntailmentBank and STREET benchmarks, using T5-large as the base model. Evaluation metrics included leaf nodes, steps, and reasoning graph accuracy. Results showed that SEER outperformed existing methods across multiple datasets.
Results
SEER outperforms existing RL methods by 6.9% on EntailmentBank and achieves an average improvement of 4.4% on the STREET benchmark. Compared to GPT-4, SEER improves by 4.8% to 5.2% across multiple datasets.
Applications
SEER can be used in QA systems requiring high interpretability and traceability, particularly in fields like law and medicine. Its structured reasoning capabilities enhance system transparency and trustworthiness.
Limitations & Outlook
SEER may require longer training times and significant computational resources for highly complex reasoning tasks. Future research could optimize its computational efficiency and explore more application scenarios.
Plain Language Accessible to non-experts
Imagine a factory where SEER acts like an intelligent production line management system. Traditional methods are like workers focusing on individual tasks, while SEER oversees the entire production line, ensuring each step is logical and efficient. This approach allows SEER to better explain and trace the source of each decision.
ELI14 Explained like you're 14
Imagine you're playing a complex puzzle game where each clue needs to be carefully combined to solve the mystery. SEER is like a super helper that helps you understand the relationships between each clue, allowing you to find the answer faster. It not only tells you the answer but also explains how each step was derived!
Glossary
Reinforcement Learning
A machine learning approach that trains models through rewards and penalties to optimize performance in specific tasks.
SEER uses reinforcement learning to optimize reasoning policies.
Structure-based Return
A reward mechanism that captures complex dependencies between reasoning steps.
SEER enhances reasoning performance through structure-based returns.
Generative Model
A model that generates new data samples by learning the data distribution.
SEER incorporates generative models for reasoning.
Fine-grained Reward Function
A reward mechanism that precisely delineates reasoning steps, particularly redundant ones.
SEER uses a fine-grained reward function to optimize reasoning processes.
EntailmentBank
A dataset for evaluating structured reasoning capabilities.
SEER was tested on the EntailmentBank dataset.
Open Questions Unanswered questions from this research
- 1 How to enhance SEER's efficiency without increasing computational costs?
- 2 How to optimize SEER's performance on domain-specific datasets?
Applications
Immediate Applications
Legal QA Systems
SEER can be used in legal QA systems to enhance interpretability and transparency.
Long-term Vision
Medical Diagnostic Support
SEER can be used in medical diagnostic support systems to help doctors better understand diagnostic processes.
Abstract
Elucidating the reasoning process with structured explanations from question to answer is crucial, as it significantly enhances the interpretability, traceability, and trustworthiness of question-answering (QA) systems. However, structured explanations demand models to perform intricately structured reasoning, which poses great challenges. Most existing methods focus on single-step reasoning through supervised learning, ignoring logical dependencies between steps. Moreover, existing reinforcement learning (RL) based methods overlook the structured relationships, underutilizing the potential of RL in structured reasoning. In this paper, we propose SEER, a novel method that maximizes a structure-based return to facilitate structured reasoning and explanation. Our proposed structure-based return precisely describes the hierarchical and branching structure inherent in structured reasoning, effectively capturing the intricate relationships between different reasoning steps. In addition, we introduce a fine-grained reward function to meticulously delineate diverse reasoning steps. Extensive experiments show that SEER significantly outperforms state-of-the-art methods, achieving an absolute improvement of 6.9% over RL-based methods on EntailmentBank, a 4.4% average improvement on STREET benchmark, and exhibiting outstanding efficiency and cross-dataset generalization performance. Our code is available at https://github.com/Chen-GX/SEER.